"""simple docstring"""


import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
	from transformers import (
	    FlaxBertModel,
	    FlaxCLIPVisionModel,
	    FlaxVisionTextDualEncoderModel,
	    FlaxViTModel,
	    VisionTextDualEncoderConfig,
	    VisionTextDualEncoderProcessor,
	)
	from transformers.modeling_flax_pytorch_utils import (
	    convert_pytorch_state_dict_to_flax,
	    load_flax_weights_in_pytorch_model,
	)


if is_torch_available():
	import torch

	from transformers import VisionTextDualEncoderModel

if is_vision_available():
	from PIL import Image





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   List[Any]:




						'''simple docstring'''



						if isinstance(__lowerCAmelCase  ,       collections.abc.Iterable							):
												return x
						return (x, x)






@require_flax
class   __UpperCAmelCase		:

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										pass

				def 						_a      ( self ):
										pass

				def 						_a      ( self ):
										pass

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										lowerCamelCase__   =np.abs((a - b) ).max()
										self.assertLessEqual(_lowerCamelCase      ,					_lowerCamelCase      ,					F'''Difference between torch and flax is {diff} (>= {tol}).''' )

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase=None      ,					**_lowerCamelCase ):
										lowerCamelCase__   =VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCamelCase      ,					_lowerCamelCase )

										lowerCamelCase__   =FlaxVisionTextDualEncoderModel(_lowerCamelCase )

										lowerCamelCase__   =model(input_ids=_lowerCamelCase      ,					pixel_values=_lowerCamelCase      ,					attention_mask=_lowerCamelCase )

										self.assertEqual(output["text_embeds"].shape      ,					(input_ids.shape[0], config.projection_dim) )
										self.assertEqual(output["image_embeds"].shape      ,					(pixel_values.shape[0], config.projection_dim) )

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase=None      ,					**_lowerCamelCase ):
										lowerCamelCase__   , lowerCamelCase__   =self.get_vision_text_model(_lowerCamelCase      ,					_lowerCamelCase )
										lowerCamelCase__   ={"vision_model": vision_model, "text_model": text_model}
										lowerCamelCase__   =FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCamelCase )

										lowerCamelCase__   =model(input_ids=_lowerCamelCase      ,					pixel_values=_lowerCamelCase      ,					attention_mask=_lowerCamelCase )

										self.assertEqual(output["text_embeds"].shape      ,					(input_ids.shape[0], model.config.projection_dim) )
										self.assertEqual(output["image_embeds"].shape      ,					(pixel_values.shape[0], model.config.projection_dim) )

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase=None      ,					**_lowerCamelCase ):
										lowerCamelCase__   , lowerCamelCase__   =self.get_vision_text_model(_lowerCamelCase      ,					_lowerCamelCase )
										lowerCamelCase__   ={"vision_model": vision_model, "text_model": text_model}
										lowerCamelCase__   =FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCamelCase )

										lowerCamelCase__   =model(input_ids=_lowerCamelCase      ,					pixel_values=_lowerCamelCase      ,					attention_mask=_lowerCamelCase )
										lowerCamelCase__   =output[0]

										with tempfile.TemporaryDirectory() as tmpdirname:
																model.save_pretrained(_lowerCamelCase )
																lowerCamelCase__   =FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCamelCase )

																lowerCamelCase__   =model(input_ids=_lowerCamelCase      ,					pixel_values=_lowerCamelCase      ,					attention_mask=_lowerCamelCase )
																lowerCamelCase__   =after_output[0]
																lowerCamelCase__   =np.amax(np.abs(out_a - out_a ) )
																self.assertLessEqual(_lowerCamelCase      ,					1E-3 )

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase=None      ,					**_lowerCamelCase ):
										lowerCamelCase__   , lowerCamelCase__   =self.get_vision_text_model(_lowerCamelCase      ,					_lowerCamelCase )
										lowerCamelCase__   ={"vision_model": vision_model, "text_model": text_model}
										lowerCamelCase__   =FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCamelCase )

										lowerCamelCase__   =model(
										    input_ids=_lowerCamelCase      ,					pixel_values=_lowerCamelCase      ,					attention_mask=_lowerCamelCase      ,					output_attentions=_lowerCamelCase )

										lowerCamelCase__   =output.vision_model_output.attentions
										self.assertEqual(len(_lowerCamelCase )      ,					vision_config.num_hidden_layers )

										# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
										lowerCamelCase__   =to_atuple(vision_model.config.image_size )
										lowerCamelCase__   =to_atuple(vision_model.config.patch_size )
										lowerCamelCase__   =(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
										lowerCamelCase__   =num_patches + 1
										self.assertEqual(vision_attentions[0].shape[-3:]      ,					(vision_config.num_attention_heads, seq_len, seq_len) )

										lowerCamelCase__   =output.text_model_output.attentions
										self.assertEqual(len(_lowerCamelCase )      ,					text_config.num_hidden_layers )

										self.assertEqual(
										    text_attentions[0].shape[-3:]      ,					(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])      ,					)

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										pt_model.to(_lowerCamelCase )
										pt_model.eval()

										# prepare inputs
										lowerCamelCase__   =inputs_dict
										lowerCamelCase__   ={k: torch.tensor(v.tolist() ) for k, v in flax_inputs.items()}

										with torch.no_grad():
																lowerCamelCase__   =pt_model(**_lowerCamelCase ).to_tuple()

										lowerCamelCase__   =fx_model(**_lowerCamelCase ).to_tuple()
										self.assertEqual(len(_lowerCamelCase )      ,					len(_lowerCamelCase )      ,					"Output lengths differ between Flax and PyTorch" )
										for fx_output, pt_output in zip(fx_outputs[:4]      ,					pt_outputs[:4] ):
																self.assert_almost_equals(_lowerCamelCase      ,					pt_output.numpy()      ,					4E-2 )

										# PT -> Flax
										with tempfile.TemporaryDirectory() as tmpdirname:
																pt_model.save_pretrained(_lowerCamelCase )
																lowerCamelCase__   =FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCamelCase      ,					from_pt=_lowerCamelCase )

										lowerCamelCase__   =fx_model_loaded(**_lowerCamelCase ).to_tuple()
										self.assertEqual(len(_lowerCamelCase )      ,					len(_lowerCamelCase )      ,					"Output lengths differ between Flax and PyTorch" )
										for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4]      ,					pt_outputs[:4] ):
																self.assert_almost_equals(_lowerCamelCase      ,					pt_output.numpy()      ,					4E-2 )

										# Flax -> PT
										with tempfile.TemporaryDirectory() as tmpdirname:
																fx_model.save_pretrained(_lowerCamelCase )
																lowerCamelCase__   =VisionTextDualEncoderModel.from_pretrained(_lowerCamelCase      ,					from_flax=_lowerCamelCase )

										pt_model_loaded.to(_lowerCamelCase )
										pt_model_loaded.eval()

										with torch.no_grad():
																lowerCamelCase__   =pt_model_loaded(**_lowerCamelCase ).to_tuple()

										self.assertEqual(len(_lowerCamelCase )      ,					len(_lowerCamelCase )      ,					"Output lengths differ between Flax and PyTorch" )
										for fx_output, pt_output_loaded in zip(fx_outputs[:4]      ,					pt_outputs_loaded[:4] ):
																self.assert_almost_equals(_lowerCamelCase      ,					pt_output_loaded.numpy()      ,					4E-2 )

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										lowerCamelCase__   =VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCamelCase      ,					_lowerCamelCase )

										lowerCamelCase__   =VisionTextDualEncoderModel(_lowerCamelCase )
										lowerCamelCase__   =FlaxVisionTextDualEncoderModel(_lowerCamelCase )

										lowerCamelCase__   =convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,					_lowerCamelCase )
										lowerCamelCase__   =fx_state

										self.check_pt_flax_equivalence(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										lowerCamelCase__   =VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCamelCase      ,					_lowerCamelCase )

										lowerCamelCase__   =VisionTextDualEncoderModel(_lowerCamelCase )
										lowerCamelCase__   =FlaxVisionTextDualEncoderModel(_lowerCamelCase )

										lowerCamelCase__   =load_flax_weights_in_pytorch_model(_lowerCamelCase      ,					fx_model.params )

										self.check_pt_flax_equivalence(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )

				def 						_a      ( self ):
										lowerCamelCase__   =self.prepare_config_and_inputs()
										self.check_model_from_pretrained_configs(**_lowerCamelCase )

				def 						_a      ( self ):
										lowerCamelCase__   =self.prepare_config_and_inputs()
										self.check_vision_text_dual_encoder_from_pretrained(**_lowerCamelCase )

				def 						_a      ( self ):
										lowerCamelCase__   =self.prepare_config_and_inputs()
										self.check_save_load(**_lowerCamelCase )

				def 						_a      ( self ):
										lowerCamelCase__   =self.prepare_config_and_inputs()
										self.check_vision_text_output_attention(**_lowerCamelCase )

				@is_pt_flax_cross_test
				def 						_a      ( self ):
										lowerCamelCase__   =self.prepare_config_and_inputs()
										lowerCamelCase__   =config_inputs_dict.pop("vision_config" )
										lowerCamelCase__   =config_inputs_dict.pop("text_config" )

										lowerCamelCase__   =config_inputs_dict

										self.check_equivalence_pt_to_flax(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )
										self.check_equivalence_flax_to_pt(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )






				@slow
				def 						_a      ( self ):
										lowerCamelCase__   , lowerCamelCase__   =self.get_pretrained_model_and_inputs()

										lowerCamelCase__   =model_a(**_lowerCamelCase )
										lowerCamelCase__   =outputs[0]

										with tempfile.TemporaryDirectory() as tmp_dirname:
																model_a.save_pretrained(_lowerCamelCase )
																lowerCamelCase__   =FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCamelCase )

																lowerCamelCase__   =model_a(**_lowerCamelCase )
																lowerCamelCase__   =after_outputs[0]
																lowerCamelCase__   =np.amax(np.abs(out_a - out_a ) )
																self.assertLessEqual(_lowerCamelCase      ,					1E-5 )






@require_flax
class   __UpperCAmelCase		(      __lowerCAmelCase , unittest.TestCase      ):

				def 						_a      ( self ):
										lowerCamelCase__   =FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
										    "hf-internal-testing/tiny-random-vit"      ,					"hf-internal-testing/tiny-bert"      ,					vision_from_pt=_lowerCamelCase      ,					text_from_pt=_lowerCamelCase      ,					)
										lowerCamelCase__   =13
										lowerCamelCase__   =floats_tensor(
										    [
										        batch_size,
										        model.config.vision_config.num_channels,
										        model.config.vision_config.image_size,
										        model.config.vision_config.image_size,
										    ] )
										lowerCamelCase__   =ids_tensor([batch_size, 4]      ,					model.config.text_config.vocab_size )
										lowerCamelCase__   =random_attention_mask([batch_size, 4] )
										lowerCamelCase__   ={"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

										return model, inputs

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										lowerCamelCase__   =FlaxViTModel(_lowerCamelCase )
										lowerCamelCase__   =FlaxBertModel(_lowerCamelCase )
										return vision_model, text_model






				def 						_a      ( self ):
										lowerCamelCase__   =FlaxViTModelTester(self )
										lowerCamelCase__   =FlaxBertModelTester(self )
										lowerCamelCase__   =vit_model_tester.prepare_config_and_inputs()
										lowerCamelCase__   =bert_model_tester.prepare_config_and_inputs()

										lowerCamelCase__   , lowerCamelCase__   =vision_config_and_inputs

										lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =text_config_and_inputs

										# make sure that cross attention layers are added
										return {
										    "text_config": text_config,
										    "vision_config": vision_config,
										    "pixel_values": pixel_values,
										    "attention_mask": attention_mask,
										    "input_ids": input_ids,
										    "token_type_ids": token_type_ids,
										}






@require_torch
class   __UpperCAmelCase		(      __lowerCAmelCase , unittest.TestCase      ):

				def 						_a      ( self ):
										lowerCamelCase__   =FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
										    "hf-internal-testing/tiny-random-clip"      ,					"hf-internal-testing/tiny-bert"      ,					vision_from_pt=_lowerCamelCase      ,					text_from_pt=_lowerCamelCase      ,					)
										lowerCamelCase__   =13
										lowerCamelCase__   =floats_tensor(
										    [
										        batch_size,
										        model.config.vision_config.num_channels,
										        model.config.vision_config.image_size,
										        model.config.vision_config.image_size,
										    ] )
										lowerCamelCase__   =ids_tensor([batch_size, 4]      ,					model.config.text_config.vocab_size )
										lowerCamelCase__   =random_attention_mask([batch_size, 4] )
										lowerCamelCase__   ={"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

										return model, inputs

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										lowerCamelCase__   =FlaxCLIPVisionModel(_lowerCamelCase )
										lowerCamelCase__   =FlaxBertModel(_lowerCamelCase )
										return vision_model, text_model






				def 						_a      ( self ):
										lowerCamelCase__   =FlaxCLIPVisionModelTester(self )
										lowerCamelCase__   =FlaxBertModelTester(self )
										lowerCamelCase__   =clip_model_tester.prepare_config_and_inputs()
										lowerCamelCase__   =bert_model_tester.prepare_config_and_inputs()

										lowerCamelCase__   , lowerCamelCase__   =vision_config_and_inputs

										lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =text_config_and_inputs

										# make sure that cross attention layers are added
										return {
										    "text_config": text_config,
										    "vision_config": vision_config,
										    "pixel_values": pixel_values,
										    "attention_mask": attention_mask,
										    "input_ids": input_ids,
										    "token_type_ids": token_type_ids,
										}






@require_flax
@require_vision
class   __UpperCAmelCase		(      unittest.TestCase      ):

				@slow
				def 						_a      ( self ):
										lowerCamelCase__   =FlaxVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian"      ,					logit_scale_init_value=1.0 )
										lowerCamelCase__   =VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian" )

										lowerCamelCase__   =Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
										lowerCamelCase__   =processor(
										    text=["una foto di un gatto", "una foto di un cane"]      ,					images=_lowerCamelCase      ,					padding=_lowerCamelCase      ,					return_tensors="np" )

										lowerCamelCase__   =model(**_lowerCamelCase )

										# verify the logits
										self.assertEqual(outputs.logits_per_image.shape      ,					(inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
										self.assertEqual(
										    outputs.logits_per_text.shape      ,					(inputs.input_ids.shape[0], inputs.pixel_values.shape[0])      ,					)

										lowerCamelCase__   =np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]] )

										self.assertTrue(np.allclose(outputs.logits_per_image      ,					_lowerCamelCase      ,					atol=1E-3 ) )



"""simple docstring"""


from __future__ import annotations

a				='#'






class   __UpperCAmelCase		:

				def __init__( self ):
										lowerCamelCase__   ={}

				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   =self._trie
										for char in text:
																if char not in trie:
																						lowerCamelCase__   ={}
																lowerCamelCase__   =trie[char]
										lowerCamelCase__   =True

				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   =self._trie
										for char in prefix:
																if char in trie:
																						lowerCamelCase__   =trie[char]
																else:
																						return []
										return self._elements(_lowerCamelCase )






				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   =[]
										for c, v in d.items():
																lowerCamelCase__   =[" "] if c == END else [(c + s) for s in self._elements(_lowerCamelCase )]
																result.extend(_lowerCamelCase )
										return tuple(_lowerCamelCase )





a				=Trie()
a				=('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
	trie.insert_word(word)





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   tuple:




						'''simple docstring'''



						lowerCamelCase__   =trie.find_word(__lowerCAmelCase							)
						return tuple(string + word for word in suffixes							)





def      lowerCamelCase_					(   )				->   None:




						'''simple docstring'''



						print(autocomplete_using_trie("de"							)							)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()


